# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-

import time

from os.path import join as opj

import os

from nipype.interfaces.io import SelectFiles, DataSink, DataGrabber

from nipype.interfaces.spm import (OneSampleTTestDesign, EstimateModel,

EstimateContrast, Threshold)

from nipype.interfaces.utility import IdentityInterface

from nipype.pipeline.engine import Workflow, Node

import nipype.interfaces.utility as niu

from nipype.interfaces.fsl import Overlay, Slicer

from processing_functions import choose_subs

# Start timing

start = time.time()

# Specify location of data.

data_dir = os.path.abspath(

'/data/pt_nmc002/other/narps/derivatives/')

out_dir = os.path.abspath(

'/data/pt_nmc002/other/narps/derivatives/second_lev_equal_range/')

deriv_dir = os.path.join(data_dir, 'first_lev/equalRange')

fwhm = [5]

mask = '/data/pt_nmc002/other/narps/derivatives/fmriprep/gr_mask_tmax.nii'

#template_image = '/data/pt_neunmc024/SpeechInNoise_Dyslexia_Study/MRI_Data_BIDS/derivatives/mni_template/mni1mm.nii'

# list of contrast identifiers

contrasts = [

'con_0001',

'con_0002',

'con_0003']

# collect all the con images for each contrast.

contrast_ids = list(range(1, len(contrasts) + 1))

# Infosource - a function free node to iterate over the list of subject names

infosource = Node(IdentityInterface(fields=['contrast_id']),

name="infosource")

infosource.iterables = [('contrast_id', contrasts)]

# Select files from derivatives.

templates = {'cons': '/data/pt_nmc002/other/narps/derivatives/first_lev/tmp/equalRange/*/contraste_estimate/{contrast_id}.nii'}

selectderivs = Node(SelectFiles(templates,

sort_filelist=True),

name='selectderivs')

#selectderivs.inputs.sub_id = subs

# One Sample T-Test Design - creates one sample T-Test Design

onesamplettestdes = Node(OneSampleTTestDesign(),

#overwrite=True,

name="onesampttestdes")

onesamplettestdes.inputs.explicit_mask_file = mask

# EstimateModel - estimate the parameters of the model

# Even for second level it should be 'Classical': 1.

level2estimate = Node(EstimateModel(estimation_method={'Classical': 1}),

name="level2estimate")

# EstimateContrast - estimates simple group contrast

level2conestimate = Node(EstimateContrast(group_contrast=True),

name="level2conestimate")

cont1 = ['Group', 'T', ['mean'], [1]]

level2conestimate.inputs.contrasts = [cont1]

## Create the 2nd level pipeline

secondlev = Workflow(name='secondlev', base_dir=out_dir+'/tmp')

secondlev.config['execution']['crashdump_dir'] = base_dir=out_dir + '/tmp/crash_files'

secondlev.connect([

(infosource, selectderivs, [('contrast_id', 'contrast_id')]),

(selectderivs, onesamplettestdes, [('cons', 'in_files')]),

(onesamplettestdes, level2estimate, [('spm_mat_file', 'spm_mat_file')]),

(level2estimate, level2conestimate, [('spm_mat_file', 'spm_mat_file'),

('beta_images', 'beta_images'),

('residual_image', 'residual_image')])

])

'''-----------------

| Run the jobs |

-----------------'''

secondlev.run(plugin='MultiProc', plugin_args={'n_procs': 16})

# Time again and spit out difference.

stop = time.time()

if (stop-start) < 3600:

print('Elapsed time: %.2f mins.' % ((stop-start)/60))

else:

print('Elapsed time: %.2f hours.' % ((stop-start)/60/60))